Recently, the proliferation and popularity of media content delivered over telecommunication networks and available to viewers has drastically increased. The ability to stream large collections of media content from multiple sources (such as cable television, satellite television, Internet Video-On-Demand services, home media centers, etc.) onto a television has led to an excess of available viewing material and a resulting surfeit of viewing choices available to a viewer. This can lead to the traditional practice of “channel “surfing”—that is, sequentially (or even randomly) traversing a series of streaming television channels—to further include traversing lists of recorded television and Video-On-Demand (“VOD”) offerings. This behavior can lead to inefficiency and user frustration, due to potentially missing a critical point in a television program or lengthy delays between programs of user interest. Unfortunately, existing methods for personalizing television programming via program recommendations fail to capture individual user preferences as this requires the need for logging individual television usage history. Collecting individual television usage history can be a very challenging task and typically, most efforts at logging television usage history have been performed by requesting the user to manually enter the programs the user has watched. However, not only is this usually insufficient to comprehensively model the user's interests, the process can also be inaccurate (due to small sample sizes) and/or burdensome on the user to manually maintain such logs.
In contrast, web personalization by mining usage logs for modeling a user's personal taste has been widely applied in personalization of web sites. Web personalization is the process of customizing a web site according to the needs or preferences of specific users by leveraging the knowledge acquired from the analysis of the user's recorded navigational behavior (usage data) in correlation with other information collected in the Web context, namely structure, content and user profile data.
Web site personalization is mostly performed at the web site server. This makes maintaining consistent aggregate individual logs difficult to collect and manage as the user can access the site from different machines, each with different Internet Protocol (“IP”) addresses and Media Access Control (“MAC”) addresses. As a solution to this problem, certain websites require the user to create and maintain personal accounts on their sites to benefit from their personalized services. However, as the web site usage logs are typically stored at the server, this can raise privacy issues and the usage of which may be undesirable for a user. Furthermore, even though such web sites can offer some level of personalization, it cannot be done across all websites as different websites may have different content management, authentication, and customization techniques. Naturally, this also serves as an obstacle to applying website personalization tools to television viewing, as these same personalization tools would not be able to be directly applied to usage logs of television viewing from multiple sources and content providers.
Moreover, usage statistics and patterns mined from web logs would be significantly different from television usage logs as the nature of content viewed on television can vary—viz, linear (scheduled content), Video On Demand (“VOD”), Live, Previously Recorded Content, etc., whereas web content is almost entirely at least capable of being delivered on demand. For example, linear television show programs are aired according to a schedule and the user may not have watched the content from the beginning, or the user's viewing progress may not be up to date with the program's current airing. The user's actual interest in the program then, can be significantly different from that of a VOD show, even though the user may have consistently watched both for some time.
As a further distinction, television streams, even though from multiple sources, have a predefined presentation format (e.g., scheduled/VOD/Live, etc.) and meta-data (e.g., series/synopsis/duration, etc.) For television content, the content provider has complete control over how, when and what is shown which facilitates the activity flow to be traceable to help identify personalization traits. Web content, however, is mostly available on-demand and hence with web personalization tools observing time based viewing behavior of television shows may be incomplete. Due to these distinctions, simply applying web-customization tools to television usage would not prove to be an effective means to provide personalized management of television consumption.